While natural language processing operational systems are getting more and more popularity and reaching out more type of users for instance individual users through the SIRI personal assistant application on the iPhones or the S-Translator on the Samsung Galaxy phones providing travelers with translation tools, or professional users like technical documentation translators relying on automated translation accuracy and fluency to accelerate their translation jobs through post-editing : these systems are facing one of the most challenging problem: the variety of the users input and the variety of their expectations. Think about Siri personal assistant whose goal is to handle seamlessly a huge variety of pronunciation, voice tone, elocution speed – or the S-Translator having to deal with any type of "chat" variants each user will invent to communicate – or the human translator expecting the system to follow its own specific style and terminology requirement. Usually trained on huge volume of data, these systems need to consistently adapt to each specific user using tiny bit of implicit feedbacks they would get from their usage of the system. Beyond their actual overall performance, and based on a specific application, we will show why such systems need to integrate adaptive features to keep being qualified and used as language assistants.